ricardian equivalence and the efficacy of fiscal policy in

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Ricardian equivalence and the efficacy of fiscalpolicy in AustraliaShane BrittleUniversity of Wollongong

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Publication DetailsBrittle, S, Ricardian equivalence and the efficacy of fiscal policy in Australia, Working Paper 09-10, Department of Economics,University of Wollongong, 2009, 37p.

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University of Wollongong Economics Working Paper Series 2008 http://www.uow.edu.au/commerce/econ/wpapers.html

RICARDIAN EQUIVALENCE AND THE EFFICACY OF FISCAL POLICY IN

AUSTRALIA

Shane Brittle School of Economics

University of Wollongong

WP 09-10 September 2009

RICARDIAN EQUIVALENCE AND THE EFFICACY OF FISCAL POLICY IN AUSTRALIA

Shane Brittle

School of Economics University of Wollongong

Northfields Avenue Wollongong NSW 2500

Australia

2

RICARDIAN EQUIVALENCE AND THE EFFICACY OF FISCAL POLICY IN AUSTRALIA

Shane Brittle

ABSTRACT

Events surrounding the global financial and economic crises of 2008 and 2009 have sparked a

renewed interest in discretionary fiscal policy. This paper considers whether private saving in

Australia behaves in a manner that is consistent with Ricardian equivalence, thus mitigating the

effects of fiscal policy, or conversely, if fiscal policy has some ability to influence real economic

activity. A model of private and public saving is estimated using the autoregressive distributed lag

approach (ARDL) to cointegration. This estimation procedure is advantageous due to its ability to

provide both short- and long-run coefficient estimates, and can accommodate coefficients for

structural breaks. Given that the Australian economy has been subject to a substantial amount of

structural change over the past 50 years, the estimations attempt to account for these structural

effects on long-run savings behaviour. Results indicate that while there is not a full Ricardian

response to changes in the fiscal stance, evidence suggests some partial offsetting behaviour –

implying that fiscal policy does elicit some (limited) impact on economic activity.

Keywords: Ricardian equivalence, fiscal policy, cointegration, structural breaks.

JEL Classifications: E21, E62, C22, H62.

3

1. Introduction

Research interest in fiscal policy waned over the 1990s, and for the most part of the 2000s, as the

“new consensus” on macroeconomic policy saw monetary policy (inflation targeting) assuming the

role of stabilising short-run fluctuations in prices and output in most advanced economies. Fiscal

policy over this period was increasingly directed toward the medium-term sustainability of

government balance sheets and allowing the automatic stabilisers to freely operate.

However, fiscal policy debates in Australia were reignited in the mid 2000s as the Howard

Government undertook a series of personal income tax cuts. At that time, the economy was

operating at or near full capacity with unemployment around 30-year lows. Critics argued that this

loosening of fiscal policy would only add to aggregate demand – leading to higher inflation and

interest rates.

Sharp falls in output associated with the global financial and economic crisis in 2008 and 2009 have

seen fiscal stimulus packages enacted in many countries, and a renewed interest in activist fiscal

policy. In a number of countries monetary policy had reached the zero bound on nominal interest

rates, leaving quantitative easing measures and fiscal policy to support aggregate demand. To

prevent a severe and prolonged global downturn, in late 2008 the International Monetary Fund

(Spilimbergo et al: 2008) called for a fiscal loosening across the advanced economies amounting to

at least 2 per cent of global gross domestic product (GDP). By mid 2009, Australia had

implemented fiscal stimulus packages amounting to around 3 per cent of GDP in 2008-09 and 2 per

cent of GDP in 2009-10 (Budget: 2009).

Considering the potential efficacy of fiscal policy, Hemming (et al: 2002) provides an excellent

survey of the international evidence on fiscal multipliers from simulations using macroeconomic

models and reduced-form specifications. In short, Hemming reports that positive fiscal shocks,

generated using estimated macroeconomic models, produce positive multipliers, with expenditure

multipliers in the range of 0.6 to 1.5 and tax multipliers in the range of 0.3 to 0.8; long-term

multipliers are generally smaller and some are negative. More recent estimates have been produced

by the Organisation for Economic Co-Operation and Development (2009), and the International

Monetary Fund (2009).

However, as Kennedy (et al: 2004) note, there is little empirical evidence on the efficacy of fiscal

policy in Australia, or estimates of fiscal multipliers. Perotti (2002) finds a positive short-term

impact spending multiplier of 0.6 for Australia, peaking at 0.8 after 14 quarters. Recent estimates

4

from the OECD (2009) suggest that the fiscal multiplier in Australia is 0.2 for tax cuts, and

increases up to 1.3 for direct government investment (such as infrastructure).

In contrast to fiscal policy having some impact on aggregate demand, Ricardian equivalence asserts

that fiscal deficits merely postpone taxes, and through the actions of altruistically motivated

individuals, budget deficits have no real affects on the economy. Barro (1974) considered the

effects on bond values and tax capitalisation of finite lives, imperfect capital markets, a government

monopoly in the production of bond ‘liquidity services’ and uncertainty about future tax

obligations. Within the context of an overlapping generations model, Barro showed that finite lives

will not be relevant for future tax liabilities so long as current generations are connected to future

generations by a chain of operative intergenerational transfers (Barro: 1974). This paper gave rise to

what is now known as the Ricardian equivalence theorem, or the Barro-Ricardo hypothesis. The key

result of Barro’s investigation being that so long as there is an operative intergenerational transfer,

there will be no net-wealth effect and no effect on aggregate demand; or on interest rates of a

marginal change in government debt. Essentially, under the Barro-Ricardo hypothesis deficits do

not matter, and do not have any impact on the macroeconomy.

Both Leiderman and Blejer (1988) and Seater (1993) provided in-depth overviews of the Ricardian

equivalence theorem. Surveys of previous empirical studies on Ricardian equivalence have been

produced by Gale and Orszag (2004), and Ricciuti (2003).

With little (recent) empirical knowledge on the efficacy of fiscal policy in modern economies, fiscal

stimulus policies have been enacted without a thorough understanding of the potency of these

policy actions – particularly given the marked structural changes in many developed economies

over the past two decades (such as the increased integration of global product and financial

markets). The analytical model employed in this paper considers the extent to which private saving

responds to changes in the total general government (Commonwealth, state and local) fiscal stance.

While this framework lends itself towards explaining Ricardian equivalence effects, it can also be

considered as a broad measure of the impact of fiscal policy on short- and long-run aggregate

demand. The model is estimated using the autoregressive distributed lag approach (ARDL) to

cointegration, which provides both short- and long-run coefficient estimates, but also provides the

flexibility to accommodate the introduction of coefficients for structural breaks.

However, it is likely that the Australian economy has been subject to a substantial amount of

structural change over the past 50 years. From the 1950s through to the early 1980s, the Australian

economy was heavily regulated, with markets subject to price controls and tariff protection, a fixed

exchange rate, and government controls on bank deposits, interest rates and credit. The 1980s saw a

period of rapid reform, with the floating of the dollar, removal of restrictions on credit creation,

interest rates, foreign capital inflows and other broader reforms around market pricing and removal

(or lowering) of tariffs and subsidies. Not accounting for these changes could lead to spurious

results in the econometric analysis. The Lee and Strazicich two-break unit root test is used to test

the time series properties of the data.

The following section discusses the analytical model to be estimated in this paper, along with the

expected signs of the explanatory variables. Section 3 uses unit root tests that allow for two

endogenously determined structural breaks in the individual time series. The analytical model is

then estimated through the ARDL approach to cointegration, which provides the flexibility to

incorporate structural breaks and both stationary and non-stationary time series. Conclusions are

presented in section 4.

2. Analytical framework

The relationship between private and public saving can be estimated through a model with the

following functional form:

5

t tZ e0 0 0priv pub

t tS Sα β φ= + + + (1)

where and denotes the ratio of net household plus net corporate saving (which gives total

net private saving) to GDP, and the ratio of net general (Commonwealth, local and state)

government saving to GDP, while

privtS pub

tS

tZ is a vector of control variables. This reduced-form saving

equation allows for the estimation of the private savings offset with a large number of control

variables, and is similar to that used in previous empirical studies by Haque (et al: 1999); Masson

(et al: 1998); Loayza (et al: 2000); Comley (et al: 2002); de Serres and Pelgrin (2003); and de Mello

(et al: 2004). A similar specification of this model was applied to the United States by Cotis (et al:

2006).

The vector tZ of control variables often includes conventional determinants of private saving, such

as the real interest rate, inflation, household income, social assistance payments to households,

changes in the terms of trade, and employment. Specifically:

{ }, , , , , , , ,t t t t t t t t t tZ Y AS U R INF TOT FLIB H EQ= (2)

Where:

= Household gross disposable income; tY

= Social assistance benefits to household gross disposable income; tAS

= Unemployment rate; tU

tR = Real interest rate;

tINF = Inflation rate;

= Terms of trade; tTOT

= Net foreign liabilities (proxy for financial openness); tFLIB

= Australian house price index (proxy for wealth); and tH

= Australian share price index (proxy for wealth). tEQ

The hypothesis of a strict private savings offset (Ricardian equivalence) would be supported if the

coefficient on public saving in (5.1) above, 0 1β = − , controlling for the other private saving

determinants. A negative coefficient on public savings, but less than 0, that is 0( 1 0)β− < < would

indicate a partial savings offset, and that changes in the general government sector’s fiscal stance

has measurable impacts on the wider economy.

Cotis (et al: 2006) discuss a number of reasons which could give rise to a positive coefficient on

public saving, that is, where 0 0β > . Sources of changes in the fiscal position arise not only from

changes to taxation arrangements, but also from changes in expenditures. For a positive private

savings offset, public expenditures need to be considered complimentary, with a clear distinction

between expenditures which are permanent, and those which are transitory. Permanent changes will

tend to generate negative private savings offsets through the restrictions imposed by the

intertemporal budget constraint. Temporary shocks in government spending, however, could

generate positive private saving responses, particularly when households see public and private

consumption as complements.1

1 Specifically, this arises when the marginal utility of private consumption is positively affected by public spending.

Government-subsidised health and education programmes, and government co-payment incentives for first home

buyers, could provide examples of public and private complements in consumption.

6

The coefficient on household disposable income, , is expected to be positive. As household

income may be considered a proxy for labour income in a standard life-cycle model of

consumption, an increase in household disposable income is expected to increase private saving.

Alternatively, households may suffer from consumption inertia and therefore take time to change

their consumption patterns to new levels of income.

tY

Social assistance payments to households, , are expected to negatively impact private savings.

The existence of a welfare safety net in Australia is expected to crowd out precautionary motives

for saving, and other privately-run alternatives that would encourage thrift.

tAS

Increasing levels of unemployment lowers disposable incomes, and, through a greater incidence of

liquidity constraints, lowers saving. However, increases in unemployment may increase the need for

precautionary saving. But as noted above the existence of welfare safety nets in Australia may

crowd out precautionary motives for saving. Overall the coefficient on the unemployment rate, ,

is expected to be of negative sign.

tU

The effects of inflation, tINF , and the real interest rate, tR , are somewhat ambiguous, and depend

largely on the extent of credit constraints and on the relative magnitude of income and substitution

effects. Also, higher, and/or accelerating inflation erodes the real value of debt and raises private

saving, but may also discourage holdings of assets that are not inflation-indexed.

Terms of trade shocks, , are particularly relevant for Australia given a high reliance on

commodity-based exports. This coefficient is expected to be positively correlated with private

saving to the extent that terms of trade shocks are viewed as being temporary

tTOT

2 through the Laursen-

Harberger-Metzler effect.3 Permanent shocks should not affect private saving.

As noted earlier, there has been a considerable amount of economic reform undertaken in Australia

over the past three decades, most notably the reform of Australia’s financial sector. Financial

liberalisation in Australia occurred over a decade beginning in the early 1980s, with removals of

restrictions on bank deposit rates and lending, and progressed to other significant reforms of which

the most notable were the floating of the Australian dollar in December 1983, and deregulation of 2 This historically has been the case with terms of trade shocks experienced with the Korean War, 1970s oil price

shocks, and most recently the rapid industrialisation of China. 3 According to the Laursen-Harberger-Metzler effect, an adverse (beneficial) transitory movement in the terms of trade

results in a decrease (increase) in a country’s current level of income which is larger than the decrease (increase) in its

permanent income, causing a fall (rise) in aggregate saving.

7

8

home mortgage interest rates. This period of financial deregulation lead to a marked structural shift

in the Australian economy and the development of sophisticated private markets for credit and

financial risk management. More sophisticated private credit markets also enables greater access to

personal credit, allowing households to smooth consumption.

As noted by de Mello (et al: 2004), the effect of financial liberalisation on private saving is

ambiguous, because improved access to credit may boost consumption but the removal of bank

portfolio allocation constraints, which often accompanies financial liberalisation, may result in

higher real interest rates, which encourages saving. Given the large increase in foreign capital

inflows following financial market deregulation, it may be reasonable to expect that any coefficient

representing financial openness in Australia will have a negative sign.

However, adequate proxies for financial openness are difficult to measure, and somewhat subjective

in nature. Proxies may include variables such as growth in M2 money and the ratio of household

wealth to disposable income (as used by Comley et al: 2002). However, long time series for these

variables are generally not available, with most measures only dating back to around the early

1980s at best. Alternative measures of financial openness have been suggested by Lane and Milesi-

Ferretti (2001), and include measures based around countries’ foreign assets and liabilities. Given

this, Australia’s level of net foreign liabilities may provide a good proxy for financial openness,

particularly as foreign debt has increased substantially since the financial market reforms of the

1980s. Data on Australia’s net foreign liabilities is also available back to the late 1950s.

Household wealth is expected to affect consumption/saving decisions based on permanent income

considerations. Given that most Australian households have historically tended to hold their wealth

through the family home, a house price index is used here as it is expected to provide a good proxy

for household wealth in Australia.4

A share price index is also considered as an additional measure of private wealth. Historically, the

proportion of Australian households participating directly in the sharemarket had been relatively

low – until rising markedly over the past two decades. In 2006, approximately 38 per cent of the

Australian population owned shares directly (Australian Securities Exchange: 2007),5 which places

Australia as having some of the highest (direct) share ownership rates in the world.

4 Around 70 per cent of Australian households owned their home in 2003-04 (Australian Bureau of Statistics: 2006). 5 Australian households have also been undertaking greater ownership of equities indirectly through their

superannuation savings. The Australian Securities Exchange (2007) estimates that in 2006, approximately 46 per cent of

Data has been sourced from the Australian Bureau of Statistics and the Reserve Bank of Australia.

The sample size is large in both the number of observations (192) and the time period which is

considered: 1959:3 – 2007:2.

3. Econometric methodology and empirical testing

Unit root tests

Previous studies which examine both Ricardian equivalence and fiscal multipliers usually have

examined the time series properties of variables by using the Augmented Dickey-Fuller (ADF)

(1979, 1981) or Philip-Perron (1988) unit root tests. However, these tests do not allow for the

possibility of one or more structural breaks in the time series. Perron (1989) argued that in the

presence of a structural break, the standard ADF tests are biased toward the non-rejection of the null

hypothesis. The timing of the structural break in Perron’s procedure is assumed to be known a

priori in accordance with underlying asymptotic distribution theory. Test statistics are constructed

by adding dummy variables representing different intercepts and slopes, thereby extending the

standard ADF procedure.

However, Perron’s technique was criticised by Christiano (1992) as specific break-dates may be

chosen which support the researcher’s results and a priori expectations (i.e. data mining). Since

then, a number of studies have been developed using different methodologies for endogenising the

structural breaks. These studies include Banerjee (et al: 1992), Zivot and Andrews (1992), Perron

(1997) Lumsdaine and Papell (1998), and Lee and Strazicich (2003).

Lee and Strazicich (2003) developed a two-break minimum Lagrange Multiplier (LM) unit root test

where the alternative hypothesis implies trend stationarity (referred to by the authors as “trend-

break stationarity”).6 This test allows for up to endogenous structural breaks, which may occur in

either the level or slope of a series. First consider the following data-generating process:

9

t 't ty Z eδ= + (3)

1t te e tuβ −= + (4)

the Australian population owned shares either directly via shares or indirectly via a managed fund or self managed

superannuation fund. 6 The null hypothesis is a unit root with breaks.

where ty is the data series in period , t δ is a vector of coefficients, tZ is a matrix of exogenous

variables, and is a standard white noise error term with zero mean and constant variance tu

2(iid ,t ) 0u N σ∼ , tZ is described by , to allow for a constant term, linear

time trend, and two structural breaks in level and trend where denotes the time period of the

breaks. Under the trend-break stationary alternative, the

'* *1 2 1 2, , ,t t t tt D D DT DT⎡⎣1, , ⎤⎦

BjT

jtD

1Bj≥ + 1, 2j

terms describe an intercept shift in the

deterministic trend, where for , 1jtD = t T = , and zero otherwise; jtDT describes a

change in slope of the deterministic trend, where 1jtDT = for , , and zero

otherwise.

1+Bjt T≥ 1, 2=j

The two-break minimum LM unit root test statistic is obtained from the following regression:

'1t t t i t iy d Z S y S tφ ε− −Δ = Δ + + Δ +∑ (5)

where t t x tS y Zψ δ= − − 2,...,t T=, and 1 1t y Zψ δ= − . is a de-trended series of tS ty using the

coefficients in tδ , which are estimated from the regression in first differences of tyΔ on

[ ]1 21, , ,t t t 1 2t t,Z D D DTΔ Δ Δ DTΔ 1Δ = , y and 1Z are the first observations of ty and tZ , respectively,

and Δ is the first difference operator. The standard white noise error terms is represented by tε . To

correct for serial correlation, , 1tS −Δ 1,...,I k= terms are included. The unit root hypothesis in

equation (5) is equivalent to 0φ = , and the test statistics are defined as:

Tρ φ= ⋅ (6)

τ = t-statistic for the null hypothesis 0φ = . (7)

To determine (endogenously) the location of the two breaks ( )/ , 1, 2j BjT T jλ = = , the minimum

LM unit root test uses a grid search procedure:

( )LM Infρ λ ρ λ= (8)

( )LM Infτ λτ λ= (9)

10

The LM test is corrected for autocorrelated errors by including lagged augmentation terms

as per the standard Augmented Dickey-Fuller test. The optimal lag length, k , is

determined through the general to specific procedure of Perron (1989).

, 1,...,St j j kΔ − =

The Lee and Strazicich two-break LM unit root test was conducted in GAUSS using code provided

by the authors. Again Models A and C were run, with lag lengths generated automatically through a

general to specific procedure.

11

)

Critical values for the two-break LM unit root test also vary depending on the location of the breaks

1 2( / , /B BT T T Tλ = and are symmetric around λ and (1 )λ− . Critical values for the two-break

minimum LM unit root test7 for Model C (intercept and trend break) are shown in Table 1 below,

and are drawn from Table 2 in Lee and Strazicich (2003). Critical values for the two-break LM unit

root test with change in intercept (Model A) at the 1%, 5% and 10% levels respectively are -4.55,

-3.84, and -3.50.

Table 1: Critical values for the two-break LM unit root test (Model C)

Break points λ = (TB1/T, TB2/T) Critical values

1% 5% 10%

λ = (0.2,0.4) -6.16 -5.59 -5.27

λ = (0.2,0.6) -6.41 -5.74 -5.32

λ = (0.2,0.8) -6.33 -5.71 -5.33

λ = (0.4,0.6) -6.45 -5.67 -5.31

λ = (0.4,0.8) -6.42 -5.65 -5.32

λ = (0.6,0.8) -6.32 -5.73 -5.32

Results from Model A (Table 2) suggest that with the exception of private saving (PS), all of the

variables contain a unit root with at least on statistically significant structural break.

7 Critical values are provided by Lee and Strazicich for T = 100. Unfortunately the authors do not provide critical values

for larger or smaller sample sizes.

12

Table 2: Results of the two-break LM unit root test (Model A) Variable k TB T φ = 0 Inference

lnPS 0 1997:4#, 2001:1# -5.4227* Stationary

GS 4 1976:2#,1999:2# -3.4204 Non-Stationary

lnY 8 1966:2,1987:3# -1.7050 Non-Stationary

lnFLIB 7 1971:4#,1976:4# -3.3650 Non-Stationary

lnU 4 1971:4,1974:4 -2.1289 Non-Stationary

R 4 1977:3#,1983:4# -3.0836 Non-Stationary

INF 8 1975:3#,1983:2 -2.2589 Non-Stationary

lnAS 7 1992:1#, 1998:3# -2.8172 Non-Stationary

lnTOT 7 1974:1#,1974:3 -2.4932 Non-Stationary

lnH 2 1973:3, 1980:4# -1.9984 Non-Stationary

LnEQ 3 1983:2, 1988:1# -3.3574 Non-Stationary A maximum of 8 lags was specified in GAUSS. # Denotes significance at the 5% level for the break-point dummy variables. Critical value for T φ = 0 is -3.84 at the 5% level. * Denotes significance at the 5% level.

When allowing for a break in both the level and trend of the series, Model C (Table 3) produces

quite different results. In contrast to Model A, the results in Table 3 suggest that household

disposable incomes (Y), inflation (INF), and the terms of trade (TOT) are also stationary series.

Table 3: Results of the two-break LM unit root test (Model C) Variable k TB T φ = 0 Critical value

break points Inference

lnPS 0 1997:4#, 2001:1 -6.5213* λ = (0.8,0.9) Stationary

GS 7 1974:3#,1997:2# -4.8116 λ = (0.3,0.8) Non-Stationary

lnY 6 1973:2#, 1992:3 -6.7481* λ = (0.3,0.7) Stationary

lnFLIB 8 1973:1#,1986:1# -4.3292 λ = (0.2,0.7) Non-Stationary

lnU 6 1974:2#,1988:1# -4.5601 λ = (0.3,0.6) Non-Stationary

R 4 1973:2,1985:3 -4.9872 λ = (0.3,0.6) Non-Stationary

INF 7 1973:2#,1991:4# -6.6046* λ = (0.3,0.7) Stationary

lnAS 7 1970:1, 1976:1# -5.4113 λ = (0.2,0.4) Non-Stationary

lnTOT 4 1969:4#,1995:4# -6.0485* λ = (0.2,0.8) Stationary

lnH 2 1972:2#, 1993:1# -3.9289 λ = (0.3,0.7) Non-Stationary

lnEQ 3 1973:2#, 1986:4# -5.2620 λ = (0.3,0.6) Non-Stationary

A maximum of 8 lags was specified in GAUSS. # Denotes significance at the 5% level for the break-point dummy variables. Critical values for T φ = 0 are contained in Table 1. * Denotes significance at the 5% level.

When interpreting results from the LM unit root tests, the timing of structural breaks could be a

useful guide for discerning the reliability and effectiveness of the procedure. Judgement of each

model (A or C) based upon economic theory and historical events, such as policy changes and

economic shocks (for example), can help to determine the timing of structural breaks, and whether

these changes have been sudden or gradual. Results indicate that structural changes have generally

coincided with a number of significant events over the past few decades, including:

• the 1960s resources boom;

• the expansion of social welfare programmes (Whitlam Government);

• oil price (terms of trade) and inflation shocks in the 1970s;

• the extensive period of financial deregulation in the 1980s; and

• the 1990-91 recession.

Cointegration

Conventional cointegration procedures (such as that of Johansen (1991, 1995), usually require that

all data entering into an equation be non-stationary. As the unit root tests undertaken above suggest

that the ratio of private saving to GDP is a stationary time series, conventional cointegration

techniques cannot be used to estimate the analytical model. Further, the unit root tests also

suggested that each data series contains at least one structural break. This further complicates the

use of cointegration techniques as conventional cointegration methods cannot account for

endogenous structural breaks. While recent econometric developments allow for cointegration

testing in the presence of structural breaks, these techniques are currently in their early stages of

development and often can only accommodate one structural break (earlier techniques such as that

of Gregory and Hansen (1996) also require all data to be non-stationary). To overcome these

difficulties, the analytical model will be estimated through the autoregressive distributed lag

(ARDL) approach to cointegration (see Pesaran and Shin 1998; Pesaran et al 1996; and Pesaran et

al 2001). This technique allows for a greater degree of flexibility – allowing for both stationary and

non-stationary data – and can accommodate additional variables that can represent structural breaks.

Following Pesaran (et al: 2001) the ARDL technique involves two steps for estimating the

cointegrating relationship. Under the first step, the existence of a long-run cointegrating relationship

is tested. If a long-run cointegrating relationship is found, the second step involves estimating both

the long and short-run coefficients. An intercept and trend term will be added to the estimation of

the model – particularly as the united root tests considered in the previous section indicated that the

dependent variable (PS) is stationary – and a visual inspection of the ratio of private saving to GDP

indicates a considerable downward trend in the data series. Therefore, the ARDL model is a general

ECM with unrestricted intercept and trend:

13

t t 1

'0 1 1 . 1

1'

p

t yy t yx x t i t ii

y a a t y x z w xπ π−

− − −=

Δ = + + + + Ψ Δ + Δ +∑ ε (10)

where and . As noted above, the first step of the ARDL procedure involves testing for

a cointegrating relationship. This step tests for the absence of any level relation between

0 0a ≠ 1 0a ≠

ty and tx

via the exclusion of the lagged level variables 1ty − and 1tx − in equation (7.6). Persaran (et al: 2001)

define the F-statistic tests for the null hypotheses as 0 : 0yyyy

π πH = , ' and the

alternative hypotheses as ,

.0 .:yx x

yx xH π π = 0

1 : 0yyyyH π π ≠ .

1yx xπ

.: yx xH π 0 '≠ . The joint null hypothesis for (10) is

given by:

(11) .0 0 0

yy yx xH H Hπ π= ∩

and the alternative hypothesis is correspondingly stated as:

(12) .1 1 1

yy yx xH H Hπ π= ∪

The asymptotic distribution of the F-statistics are non-standard under the null hypothesis of no

cointegrating relationship between the variables, regardless of the order of integration of the

variables being considered. The calculated F-statistic is compared with the critical values provided

in Pesaran (et al: 2001). The null hypothesis of no cointegration is rejected if the calculated F-

statistic is greater than the upper bound critical value. If the calculated F-statistic falls below the

lower bound, then the null hypothesis of no cointegration cannot be rejected. The result is

inconclusive if the calculated F-statistic lies between the upper and lower bound critical values.

The ARDL specification for equation (1) is as follows:

0 11 1 1 1

p p p p

t i t i i t i i t i ii i i i

PS t PS GS Y ASα α δ β φ ϕ− − −= = = =

Δ = + + Δ + Δ + Δ + Δ +∑ ∑ ∑ ∑ t i−

− +

− +

t

1 1 1 1 1

p p p p p

i t i i t i i t i i t i i t ii i i i i

U R INF TOT FLIBγ τ υ ρ ψ− − − −= = = = =

Δ + Δ + Δ + Δ + Δ∑ ∑ ∑ ∑ ∑

1 1 2 1 3 1 4 1 5 11 1

p p

i t i i t i t t t t ti i

H EQ PS GS Y AS Uξ ω λ λ λ λ λ− − − − − −= =

Δ + Δ + + + + +∑ ∑

6 1 7 1 8 1 9 1 10 1 11 1t t t t t tR INF TOT FLIB H EQ uλ λ λ λ λ λ− − − − − −+ + + + + + (13)

where and have been shortened to PS and GS respectively. In the ARDL specification

above, the summation signs represent the error correction dynamics, while the second section of the

equation, denoted by

privtS pub

tS

iλ , represents the long run relationship. The null hypothesis of no

cointegration in equation (13) is given by:

14

0 1 2 3 4 5 6 7 8 9 10 11: 0H λ λ λ λ λ λ λ λ λ λ λ= = = = = = = = = = =

or equivalently as:

( , , , , , , , , ,PSF PS GS Y AS U R INF TOT FLIB H EQ)

The corresponding alternative hypothesis is:

1 2 3 4 5 6 7 8 9 10 110, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0λ λ λ λ λ λ λ λ λ λ λ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠

As noted earlier, the relevant test statistic here is the F-statistic for the joint significance of the

coefficients, and as we are dealing with quarterly data, a maximum of 4 lags is included.

Table 4: Results from bounds test on equation (13) – 1959:3 to 2006:2 Dep. Var. F-statistic Probability Conclusion

( , , , , , , , , ,PS )F PS GS Y AS U INF R TOT FLIB H EQ 3.4906* 0.000 Cointegration

( , , , , , , , , ,GS )F GS PS Y AS U INF R TOT FLIB H EQ 2.4126 0.009 Inconclusive

( , , , , , , , , ,Y )F Y PS GS AS U INF R TOT FLIB H EQ 2.2677 0.015 No cointegration

( , , , , , , , , ,AS )F AS PS GS Y U INF R TOT FLIB H EQ 2. 4465 0.008 Inconclusive

( , , , , , , , , ,U )F U PS GS Y AS INF R TOT FLIB H EQ 3.0196 0.001 Inconclusive

( , , , , , , , , ,R )F R PS GS Y AS U INF TOT FLIB H EQ 2.1676 0.020 No cointegration

( , , , , , , , , ,INF )F INF PS GS Y AS U R TOT FLIB H EQ 2. 0838 0.026 No cointegration

( , , , , , , , , ,TOT )F TOT PS GS Y AS U INF R FLIB H EQ 3.5018* 0.000 Cointegration

( , , , , , , , , ,FLIB )F FLIB PS GS Y AS U INF R TOT H EQ 1.7875 0.063 No cointegration

( , , , , , , , , ,H )F H PS GS Y AS U INF R TOT FLIB EQ 3.1870 0.001 Inconclusive

( , , , , , , , , ,EQ )F EQ PS GS Y AS U INF R TOT FLIB H 1.8996 0.045 No cointegration

Asymptotic critical value bounds are obtained from Table CI(iii), Case V: unrestricted intercept and unrestricted trends for k=10 (Persaran et al: 2001). Lower bound I(0)=2.33 and Upper bound I(1)=3.46 at the 5% significance level. * Denotes significance at the 5% level. ** Denotes significance at the 1% level. Where private savings is the dependent variable, the calculated F-statistic of 3.4906 is greater than

the upper bound critical value at the 5 per cent level, which rejects the null hypothesis of no

cointegration – implying a long-run level relationship between the variables (Table 4). Considering

the possibility of reverse causation, where government savings is the long-run dependent variable,

the calculated F-statistic of 2.4126 falls into the inconclusive region. Consequently, reverse

causation cannot be ruled-out. Where the cointegration tests are undertaken with different

dependent variables, the results also suggest a long-run relationship between the variables, and that

15

16

Y, R, INF, FLIB, and EQ act as the long-run forcing variables for private saving. While results in

Table 1 show inconclusive results for social assistance payments (AS), unemployment (U), and

house prices (H), the subsequent estimations of the short- and long-run parameters may yield

further information on the significance of these variables.

The structural breaks identified above may be accounted for by the inclusion of break-point dummy

variables in the ARDL model. The structural breaks to be included in the ARDL specification are:

B1969:1 = 1960s resources boom;

B1973:3 = expansion of social welfare programmes (Whitlam Government); oil price shocks

and inflation8;

B1984:1 = floating of the Australian dollar9, including broader financial market liberalisation;

and

B1990:1 = onset of recession in the early 1990s.

Estimation results

The estimated long-run coefficient estimates for equation (13) are provided in Table 5.10 With the

exception of the unemployment rate (U), all variables have the expected sign, although the wealth

variables will be discussed in greater detail below. For the level of government savings (GS), the

results suggest that over the long run, changes in general government saving are offset by changes

in private savings by almost half (-0.44). This implies that the behavioural response of households

and corporations is not fully Ricardian, and that fiscal policy has a (partial) flow through to the real

economy – potentially impacting output, real interest rates, the exchange rate, and subsequently the

current account. The value of this coefficient is also similar to the results of Comley (et al: 2002),

who estimated a long-run private savings offset coefficient for Australia of -0.5. However, it is

important to note here that Comley’s estimated long-run coefficient was not statistically significant,

possibly due to having a much smaller sample (1981:1-2002:2).

8 While two breaks may have been included for each of these effects, the close proximity of both breaks would mean

that the inclusion of separate dummy variables for each could increase the likelihood of serial correlation in the

regression estimates. 9 The floating of the Australian dollar is considered to be the most significant of the broader financial market reforms

undertaken over the decade from the late 1970s though to the late 1980s. 10 The appropriate lag length was chosen according to the Schwarz Bayesian Criterion.

17

The estimated Australian private savings offset of -0.44 is however lower than some estimates

derived through international panel studies. Considering private savings across a panel of 21 OECD

countries, de Mello (et al: 2004) estimated a long-run private savings offset coefficient of around

-0.75; implying that changes in the fiscal stance are almost fully offset by corresponding changes in

private saving. Following an analytical model similar to that used here, and to that employed by de

Mello (et al: 2004), Cotis (et al: 2006) estimated a long-run private savings offset of around two

thirds for a panel of 16 OECD countries. Isolating impacts on the United States, Cotis (et al: 2006)

estimated a positive long-run private savings coefficient – implying that US households behave in a

non-Ricardian manner.11

Table 5: Estimated long-run coefficients for equation (13) ARDL (1,0,1,2,0,0,0,0,0,0,0) selected lags based on Schwarz Bayesian Criterion

Variable Coefficient Standard Error T-Ratio Probability

Constant -0.2564 0.1157 -2.2152* 0.028

Trend 0.0003 0.0003 0.9729 0.332

GS -0.4438 0.1178 -3.7673** 0.000

Y 0.4241 0.1409 3.0100* 0.003

U 0.1571 0.2082 0.7542 0.452

R 0.0301 0.0729 0. 4128 0.680

INF -0.1460 0.1094 -1.3340 0.184

AS -0.4579 0.2145 -2.1342* 0.034

TOT 0.0008 0.0002 3.9830** 0.000

FLIB -0.0364 0.0155 -2.3410* 0.020

H -0.0066 0.0127 -0.5153 0.607

EQ 0.0179 0.0106 1.6806 0.095

B1969 0.0029 0.0062 0.4685 0.640

B1973 -0.0161 0.0106 -1.5082 0.133

B1984 -0.0035 0.0066 -0.5388 0.591

B1990 -0.0151 0.0078 -1.9209 0.056 * Denotes significance at the 5% level. ** Denotes significance at the 1% level.

For the remaining variables in Table 5, the results indicate that for a 1 per cent rise in household

gross disposable income (Y), the ratio of private savings to GDP increases by 0.42 per cent. This

also implies a marginal propensity to consume of approximately 0.6 – which is consistent with

Australian National Accounts data (which indicates that the consumption share of GDP in Australia 11 Changes in public saving result from both taxation and expenditure. While permanent expenditures will generate an

increase in private saving through the intertemporal budget constraint, temporary expenditure shocks can generate

positive private saving offsets (particularly when households see public and private consumption as complements; for

example, rebates and co-payments).

of 60 per cent). Rising levels of social assistance payments to households (AS) are estimated to have

a negative impact on private savings over the long-run, with the ratio of private saving to GDP

declining by around 0.46 per cent for each one per cent increase in social assistance payments to

households. Australia’s terms of trade are (TOT) is estimated to have a small, although statistically

significant, positive impact on private savings over the long run. As expected, financial

liberalisation has a negative impact on private savings over the long run. For the unemployment rate

(U), the real interest rate (R), and inflation (INF), the results in Table 5 indicate that these variables

do not have a statistically significant long-run impact on the level of private saving in Australia.

Both of the wealth variables present some interesting results. Changes in the prices of household

assets (and the returns derived from these) will affect household consumption and saving.

Additionally, as the dependent variable is private saving (which includes corporate saving), changes

in wealth will also affect business borrowing and investment decisions. Results here indicate that

wealth from housing does not exert a statistically significant impact on private saving over the long

run, although it is of the expected sign. Given that most Australian’s hold wealth through the family

home, this is somewhat surprising. Equity prices appear to have had a statistically significant (albeit

at the 10 per cent level) impact on private saving over the long run. The positive sign of this

coefficient is curious, and suggests that for a 1 per cent rise in equity prices, the ratio of private

saving to GDP rises by around 0.02 per cent. This positive response may be somewhat indicative of

the broad shift toward equity investment, particularly the indirect investment occurring through

households’ accumulation of assets in superannuation.

Of the dummy variables included in the estimation, only the structural break coinciding with the

early 1990s recession (B1990) is estimated to have had a statistically significant (at the 10 per cent

level) long-run impact on the private savings ratio.

The short-run error correction estimates are presented in Table 6. In the short-run, the error

correction equation indicates a private saving offset of one quarter (-0.25) to changes in government

saving. The error correction term, ( 1)ecm − , is of the correct sign and statistically significant –

indicating that deviations from the long-run rate of private saving are corrected by over 50 per cent

in the next period, which is a relatively fast pace of adjustment back to equilibrium. While the

unemployment rate (U) was statistically insignificant in the long-run relationship, the estimated

coefficient here is of the correct sign, and significant at the 10 per cent level, whilst the lagged value

of unemployment is significant at the 1 per cent level. This suggests that the unemployment rate

negatively impacts private saving in the short-run only, which would be consistent with the impact

of temporary shocks to output.

18

Table: 6 Error correction representation of equation (13) ARDL (1,0,1,2,0,0,0,0,0,0,0) selected lags based on Schwarz Bayesian Criterion


Constant -0.1469 0.0692 -2.1224* 0.035

Trend 0.0002 0.0002 0.9838 0.327

GSΔ -0.2544 0.0675 -3.7637** 0.000

YΔ 0.5249 0.0747 7.0231** 0.000

UΔ -0.3919 0.2228 -1.7593 0.080

( 1)UΔ − -0.7711 0.2184 -3.5302** 0.001

RΔ 0.0172 0.0419 0.4119 0.681

INFΔ -0.0804 0.0593 -1.3568 0.177

ASΔ -0.2624 0.1208 -2.1718* 0.031

TOTΔ 0.0004 0.0001 3.8787** 0.000

FLIBΔ -0.0208 0.0086 -2.4049* 0.017

HΔ -0.0037 0.0072 -0.5176 0.605

EQΔ 0.0102 0.0060 1.7059 0.090

1969BΔ 0.0016 0.0036 0.4645 0.643

1973BΔ -0.0092 0.0061 -1.5230 0.130

1984BΔ -0.0020 0.0038 -0. 5415 0.589

1990BΔ -0.0087 0.0047 -1.8481 0.066

( 1)ecm − -0.5732 0.0597 -9.6020** 0.000

* Denotes significance at the 5% level. ** Denotes significance at the 1% level. 0.444 * 0.424 * 0.157 * 0.03 * 0.160 * 0.458 * 0.0007 * 0.036 *ecm PS GS Y U R INF AS TOT FLIB= + − − − + + − + +

0.007 * 0.018 * 0.256 * Constant 0.0003 * Trend 0.003 * 1969 0.016 * 1973 0.004 * 1984 0.015 * 1990H EQ B B B B− + − − + + +

20.6249R =

20.5844R = F-stat [ ](17,168) 17.3865 0.000F = SER 0.0082=

RSS 0.011= DW-statistic 2.0817=

Short-run coefficient estimates for household gross disposable income (Y), and the terms of trade

(TOT) are significant at the 1 per cent level, while social assistance payments (AS), and financial

openness (FLIB) are significant at the 5 per cent level. Similar to the long-run results, the estimated

short-run coefficients for the real interest rate (R), inflation (INF) and break-point dummy variables

B1969, B1973, and B1984 are statistically insignificant. The short-run results also indicate that

housing wealth is not statistically insignificant, while wealth from equities appears to bear a

statistically significant influence (at the 10 per cent level) on the ratio of private saving to GDP in

Australia.

Diagnostic statistics from the estimations are positive (Table 7), indicating that the error terms do

not suffer from serial correlation, and are normally distributed. The model specification also

satisfies the RESET test for omitted variables and functional form.

19

Table 7: Diagnostic tests on equation (13) LM Test Statistics 2χ statistic Probability

Serial correlation a 2(4)χ 3.3784 0.497

Normality b 2(2)χ 1.5196 0.468

Functional form c 2(1)χ 0.0038 0.951

Heteroscedasticity d 2(1)χ 0.0179 0.893

* Denotes significance at the 5% level. ** Denotes significance at the 1% level. a Breusch-Godfrey LM test for serial correlation. b Jarque-Bera normality test. c Ramsey RESET test for omitted variables/functional form. d White test for heteroscedasticity.

Two subsample estimations for equation (13) will now be undertaken. These cover the period

1959:3 – 1983:4, while the second period is over 1984:1 – 2006:2. This will attempt to account for

the effects of financial market liberalisation, and a move toward a greater integration of the

Australian economy into the global financial system – particularly as the break-point dummy

variable (B1984) was not statistically significant in the earlier analysis.12

Over the first subsample period, the Australian economy was highly regulated, with a fixed

exchange rate, tariff controls, and other regulations over the financial system such as controls on

bank lending, deposits, and some interest rates (such as mortgage interest rates, overnight money

market rates, and deposit rates). Since the floating of the Australian dollar and associated financial

market reforms, foreign capital inflows into Australia have increased markedly, and there has been

a commensurate increase in financial market innovation. This integration into global capital markets

may have dampened the impact of fiscal policy on the economy. These reforms have also occurred

in concert with other reforms in the labour market, tariff reform, the establishment of free trade

arrangements with some countries, a national competition policy agenda, fiscal consolidation,

privatisation of government business enterprises, and the introduction of inflation targeting.

Private saving offsets – 1959:3 to 1983:4

Cointegration tests where private saving (PS) is the dependent variable yield an F-statistic of

3.7095, which is greater than the upper bound critical value at the 5 per cent level – implying that

the long-run level relationship between these variables is still observed over the first subsample

period (Table 8). However, where government savings is the dependent variable, the calculated F-

statistic again falls into the inconclusive zone.

12 As the financial reforms were phased over the 1980s, with the floating of the Australian dollar one of several major

reforms, the insignificance of this dummy variable is not that surprising. This implies that a gradual structural change

may have been occurring as opposed to a sudden level shift.

20


( , , , , , , , , ,PS )F PS GS Y AS U INF R TOT FLIB H EQ 3.7095* 0.001 Cointegration

( , , , , , , , , ,GS )F GS PS Y AS U INF R TOT FLIB H EQ 2.5843 0.016 Inconclusive

( , , , , , , , , ,Y )F Y PS GS AS U INF R TOT FLIB H EQ 1.1575 0.349 No cointegration

( , , , , , , , , ,AS )F AS PS GS Y U INF R TOT FLIB H EQ 3.2765 0.003 No cointegration

( , , , , , , , , ,U )F U PS GS Y AS INF R TOT FLIB H EQ 2.1103 0.045 No cointegration

( , , , , , , , , ,R )F R PS GS Y AS U INF TOT FLIB H EQ 2.1373 0.043 No cointegration

( , , , , , , , , ,INF )F INF PS GS Y AS U R TOT FLIB H EQ 1.6689 0.121 No cointegration

( , , , , , , , , ,TOT )F TOT PS GS Y AS U INF R FLIB H EQ 2.4355 0.022 Inconclusive

( , , , , , , , , ,FLIB )F FLIB PS GS Y AS U INF R TOT H EQ 2.2704 0.032 No cointegration


( , , , , , , , , ,EQ )F EQ PS GS Y AS U INF R TOT FLIB H 3.7878 0.001 Cointegration

Asymptotic critical value bounds are obtained from Table CI(iii), Case V: unrestricted intercept and unrestricted trends for k=10 (Persaran et al: 2001). Lower bound I(0)=2.43 and Upper bound I(1)=3.56 at the 5% significance level. * Denotes significance at the 5% level. ** Denotes significance at the 1% level.

For the ARDL estimation over the period 1959:3-1983:4, initial results for equation (13) were not

positive, and indicated that the errors of the estimated ARDL were serially correlated and not

normally distributed. Additionally, the estimated trend coefficient was of the wrong sign. The trend

coefficient was dropped, along with estimated coefficients for the real interest rate (R), inflation

(INF), financial openness (FLIB), and the break-point dummy variables (B1969) and (B1973) as

these variables were all statistically insignificant. Serial correlation was still apparent in the model,

and despite theory suggesting that wealth effects may explain some of the variation in private

saving behaviour; both the house and equity price series were also dropped from the model.

Removing these improved the results markedly, with the Jarque-Bera test indicating that the

residuals were normally distributed, while the Breusch-Godfrey LM test suggested that serial

correlation had also been alleviated. This left the following specification for the subsample ARDL:

01 1 1 1

p p p p

t i t i i t i i t i ii i i i

PS PS GS Y Uα δ β φ γ− − −= = = =

Δ = + Δ + Δ + Δ + Δ∑ ∑ ∑ ∑ t i− +

− +

tu

1 1 2 11 1

p p

i t i i t i t ti i

AS TOT PS GSϕ ρ λ λ− − −= =

Δ + Δ + +∑ ∑

3 1 4 1 5 1 6 1t t t tY U AS TOTλ λ λ λ− − − −+ + + + (14)

21

The estimated long-run coefficient estimates for equation (14) are provided in Table 9. For the ratio

of government saving to GDP (GS) over the period 1959:3-1983:4, the estimated coefficient is

-0.39, which is somewhat lower than the full sample estimation. This potentially suggests that with

a lower private saving offset, fiscal policy may have exerted a larger impact on the real economy

during this period. Such a result would be consistent with the structure of the economy at that time

(markets being subject to a greater degree of regulation, and less exposure to international capital

and price movements) and confirms a priori expectations regarding these policy impacts.

Table 9: Estimated long-run coefficients for equation (14) ARDL (1,0,1,0,2,0) selected lags based on Schwarz Bayesian Criterion


Constant -0.2085 0.0648 -3.2159** 0.002

GS -0.3994 0.1861 -2.1455* 0.035

Y 0.3906 0.0700 5.5746** 0.000

U -0.1998 0.2475 -0.8075 0.422

AS -0.2438 0.2855 -0.8539 0.395

TOT 0.0007 0.0003 2.6296** 0.010 * Denotes significance at the 5% level. ** Denotes significance at the 1% level.

A one per cent rise in household gross disposable income (Y) is estimated to raise the ratio of

private saving to GDP by 0.39 per cent over the first subsample, which is slightly higher than for

the full sample estimation. The terms of trade (TOT) is statistically significant, but is estimated to

only exert an extremely small impact on the private saving to GDP ratio. As expected, over this

subsample the ratio of social assistance payments to household gross disposable income (AS) and

the unemployment rate (U) are estimated to have had a statistically insignificant long-run impact on

private saving.


correction equation indicates a private saving offset of -0.23. The error correction term, ( 1)ecm − , is

of the correct sign and statistically significant – indicating that deviations from the long-run rate of

private savings are corrected by over 50 per cent in the next period. Household gross disposable

income, (Y), is statistically significant (at the one per cent level) while the estimated coefficient for

social assistance payments (AS) is markedly higher in the short-run, and includes an additional lag

coefficient for adjustment. The larger sign of this coefficient in the short run may again be

explained by the steep rise in the unemployment rate in 1974, then rising again in 1983 (where the

unemployment rate reached 10.2 per cent in the September quarter 1983) – suggesting that

households were more dependent on the welfare safety net over this period. However, it is

interesting that the results indicate that the unemployment rate is statistically insignificant in both 22

the long and short-run estimations. Prior to the large rise in unemployment during the 1970s, the

unemployment rate averaged 2 per cent over the 1960s. The introduction of expanded social welfare

programmes by the Whitlam government almost coincided with a steep rise in unemployment in

1974, which may explain this curio.13

Table 10: Error correction representation of equation (14) ARDL (1,0,1,0,2,0) selected lags based on Schwarz Bayesian Criterion


Constant -0.1216 0.0413 -2.9407* 0.004

GSΔ -0.2329 0.1021 -2.2812* 0.025

YΔ 0.4916 0.0806 6.0980** 0.000

UΔ -0.1165 0.1462 -0.7968 0.428

ASΔ -1.4175 0.3407 -4.1602** 0.000

( 1)ASΔ − -0.7800 0.3133 -2.4892* 0.015

TOTΔ 0.0004 0.0002 2.6569* 0.009

( 1)ecm − -0.5831 0.0945 -6.1691** 0.000

* Denotes significance at the 5% level. ** Denotes significance at the 1% level. 0.399 * 0.391* 0.199 * 0.244 * 0.0007 * 0.209 * Constantecm PS GS Y U AS TOT= + − + + − +

20.7104R =

20.6800R = F-stat [ ](7, 88) 30.1357 0.000F = SER 0.0078=


Diagnostic statistics for the error correction mechanism (Table 11) are positive and indicate that the

model is correctly specified. The error terms are normally distributed and the Breusch-Godfrey LM

test indicates that no serial correlation is present.



Normality b 2(2)χ 3.7570 0.153




13 In the absence of social welfare arrangements, the coefficient on unemployment could in fact be positive; inferring

that a rise in unemployment spurs an increase in precautionary saving.

23

Private saving offsets – 1984:1 to 2006:2

Cointegration tests where private saving is the dependent variable yield an F-statistic of 2.766,

which falls within the inconclusive range of the critical values at the 5 per cent level (Table 12).

Results from the bounds test also suggest reverse causation where government savings is the

dependent variable. Given the overall sample results presented earlier lend support to cointegration

the ARDL estimations will still be undertaken. The inconclusive result (and the suggested reverse

causation with government savings as the dependent variable) may in fact suggest that financial

liberalisation in Australia, leading to deeper and more open capital markets, has eroded the

transmission of changes in the government’s fiscal stance.


( , , , , , , , , ,PS )F PS GS Y AS U INF R TOT FLIB H EQ 2.7660 0.012 Inconclusive

( , , , , , , , , ,GS )F GS PS Y AS U INF R TOT FLIB H EQ 4.7084 0.000 Cointegration

( , , , , , , , , ,Y )F Y PS GS AS U INF R TOT FLIB H EQ 2.1220 0.047 Inconclusive

( , , , , , , , , ,AS )F AS PS GS Y U INF R TOT FLIB H EQ 2.6908 0.014 Inconclusive

( , , , , , , , , ,U )F U PS GS Y AS INF R TOT FLIB H EQ 3.1875 0.005 Inconclusive

( , , , , , , , , ,R )F R PS GS Y AS U INF TOT FLIB H EQ 2.6692 0.014 Inconclusive

( , , , , , , , , ,INF )F INF PS GS Y AS U R TOT FLIB H EQ 2.3367 0.029 No cointegration

( , , , , , , , , ,TOT )F TOT PS GS Y AS U INF R FLIB H EQ 3.6749 0.002 Cointegration

( , , , , , , , , ,FLIB )F FLIB PS GS Y AS U INF R TOT H EQ 2.7118 0.013 Inconclusive


( , , , , , , , , ,EQ )F EQ PS GS Y AS U INF R TOT FLIB H 4.4042 0.000 Cointegration

Asymptotic critical value bounds are obtained from Table CI(iii), Case V: unrestricted intercept and unrestricted trends for k=10 (Persaran et al: 2001). Lower bound I(0)=2.43 and Upper bound I(1)=3.56 at the 5% significance level. * Denotes significance at the 5% level. ** Denotes significance at the 1% level.

After initially estimating equation (13), the results suggested that social assistance payments as a

proportion of household disposable income (AS), inflation (INF), the real interest rate (R) and the

break-point dummy variable coinciding with the early 1990s recession (B1990) were statistically

insignificant. The following ARDL was estimated:

24

25

i t i− +

−

− +

t

0 11 1 1

p p p

t i t i i t ii i i

PS t PS GS Yα α δ β φ− −= = =

Δ = + + Δ + Δ + Δ∑ ∑ ∑

1 1 1 1

p p p p

i t i i t i i t i i t ii i i i

U TOT FLIB Hγ ρ ψ ξ− − −= = = =

Δ + Δ + Δ + Δ +∑ ∑ ∑ ∑

1 1 2 1 3 1 4 11

p

i t i t t t ti

EQ PS GS Y Uω λ λ λ λ− − − −=

Δ + + + +∑

5 1 6 1 7 1 8 1t t t tTOT FLIB H EQ uλ λ λ λ− − − −+ + + + (15)

The estimated long-run coefficient estimates are provided in Table 13. For the ratio of government

saving to GDP (GS) over the period 1984:1-2006:2, the estimated coefficient is -0.39, and

statistically significant only at the 10 per cent level. For the other variables, a one per cent rise in

household gross disposable income (Y) is estimated to raise the ratio of private savings to GDP by

0.43 per cent. Net foreign liabilities (FLIB) are also significant at the 1 per cent level – and indicate

that Australian financial markets have become more integrated with global capital flows. The

long-run coefficient on the terms of trade (TOT) is slightly higher than the previous estimations,

which possibly indicates that as Australia has become more integrated with the global economy and

that international price determination for traded goods may be exerting a greater influence over

household incomes, consumption and saving. The house price index is now statistically

insignificant, while equity prices remain significant at the 10 per cent level.

Table 13: Estimated long-run coefficients for equation (15) ARDL (2,1,0,2,0,1,0,0) selected lags based on Schwarz Bayesian Criterion


Constant -0.3901 0.2294 -1.7001 0.093

Trend -0.0006 0.0004 -1.2942 0.200

GS -0.3855 0.2386 -1.6160 0.110

Y 0.4338 0.2371 1.8295 0.071

U 0.4296 0.3463 1.2407 0.219

TOT 0.0012 0.0003 3.5862** 0.001

FLIB -0.0700 0.0227 -3.0776** 0.003

H 0.0202 0.0242 0.8328 0.408

EQ 0.0341 0.0187 1.8232 0.072 * Denotes significance at the 5% level. ** Denotes significance at the 1% level.


correction equation indicates a private savings offset of -0.40 to changes in government saving,

which is both statistically significant and roughly equivalent to the estimated long-run coefficient.

The error correction term, , is of the correct sign and statistically significant – indicating

that deviations from the long-run rate of private savings are corrected by around 50 per cent in the

next period.

( 1)ecm −

Table 14: Error correction representation of equation (15) ARDL (2,1,0,2,0,1,0,0) selected lags based on Schwarz Bayesian Criterion


Constant -0.1816 0.1008 -1.8006 0.076

Trend -0.0003 0.0002 -1.3609 0.177

( 1)PSΔ − -0.1769 0.0805 -2.1976* 0.031

GSΔ -0.3977 0.1049 -3.7921** 0.000

YΔ 0.2019 0.1110 1.8187 0.073

UΔ -0.4623 0.3714 -1.2445 0.217

( 1)UΔ − -1.1101 0.3230 -3.4367** 0.001

TOTΔ 0.0006 0.0002 3.4544** 0.000

FLIBΔ -0.0776 0.0189 -4.0914* 0.000

HΔ -0.0094 0.0108 0.8707 0.387

EQΔ -0.0158 0.0078 2.0123* 0.048

( 1)ecm − -0.4654 0.0906 -5.1340** 0.000

* Denotes significance at the 5% level. ** Denotes significance at the 1% level. 0.385 * 0.434 * 0.429 * 0.001 * 0.070 * 0.020 * 0.034 *ecm PS GS Y U TOT FLIB H EQ= + − − − + − − +

0.390 * 0.006 * TrendINPT + 2

0.6690R = 2

0.6072R = F-stat [ ](11, 78) 13.7805 0.000F = SER 0.0073=


Diagnostic statistics for the error correction mechanism (Table 15) are positive, and indicate that the

model is correctly specified.



Normality b 2(2)χ 0.4971 0.780




26

27

4. Conclusions

Results from the estimations suggest that while there is no full Ricardian response in Australia to

changes in the fiscal stance, fiscal policy has some ability to impact the real economy. Estimates

suggest a long-run private saving offset around one half, and between -0.25 and -0.40 in the short

run.

While the lower short-run offsets revealed through the error correction mechanisms indicate that

nominal and real frictions and/or rigidities prevent some proportion of the offsetting behaviour

occurring more quickly, this result is consistent with Keynesian models – suggesting that fiscal

policy has a greater ability to influence the real economy over the short term (particularly where

some households are liquidity constrained). While full Ricardian equivalence has not been observed

in the results, they do suggest that over the longer-term, households and organisations are more

forward-looking, and exhibit some partial Ricardian behaviour.

A critical question this paper has also sought to answer is the extent to which the development of

the Australian financial sector (and increased integration into global capital markets) may have

dampened the impact of fiscal policy on the real economy. Estimates of the long-run coefficient on

government saving over the two subsamples (1959:3-1983:4 and 1984:1-2006:2) did not provide

any clear indication that this may be occurring (both sets of estimations produced a long-run

coefficient on government saving around -0.39). However, the short-run error correction

coefficients were markedly different, with the second subsample estimation yielding a short-run

private saving offset that was close to the long-run estimate (-0.40).

Results also confirm greater linkages between Australia and the global economy. While the

coefficient on net foreign liabilities (FLIB), which was taken as a proxy for financial market

openness, was statistically insignificant in the first subsample, this coefficient was found to be

statistically significant in the second subsample. The negative value of this coefficient (-0.07)

suggests that greater access to international capital has lowered private saving. The coefficient on

the terms of trade (TOT) was also higher in the second subsample, which indicates that Australia

may have been deriving higher income from commodities over this period.

While results in this paper suggest that households are not fully Ricardian, fiscal policy can

nonetheless exert some impact on real economic activity. However, it is unreasonable to expect that

any discretionary fiscal policy actions will have a one-for-one impact on the real economy. To the

extent that households anticipate higher (lower) taxes in the future, they will partially offset any

policy action through higher (lower) saving. Where policymakers see a need for discretionary

28

policy, it is important to consider the composition of expenditure, as policies directed at particular

sectors or households will likely generate different impacts.

While there is a role for activist fiscal policy under extreme economic circumstances, the results

also indicate that fiscal policy will only exert a partial impact on activity. It would take substantial

movements in the fiscal stance (greater than 1 per cent of GDP) to have a marked impact on the real

economy. Such large movements in the fiscal position only exacerbate the risks of poor policy,

which includes a risk of excessive debt accumulation, entrenched expenditures and pro-cyclical

impacts (arising from poorly timed policy).

29

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